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https://cran.r-project.org/src/contrib/Archive/QuasiSeq/

Software package to apply the QL, QLShrink and QLSpline methods to quasi-Poisson or quasi-negative binomial models for identifying differentially expressed genes in RNA-seq data.

Proper citation: QuasiSeq (RRID:SCR_001715) Copy   

•   Source: SciCrunch Registry

http://incf.org/about/programs/modeling/blue-gene-access

Through this site, INCF provides he neuroinformatics community with access to an IBM Blue Gene/L supercomputer. INCF owns a share of a BlueGene/L (BG/L) supercomputer located at the Parallel Computer Center (PDC) at The Royal Institute of Technology (KTH) in Stockholm. Allocations are now available through the INCF Secretariat. During an initial evaluation phase, a limited numbers of large-scale computing projects will be selected, based on the suitability of the project for supercomputing. Research groups with limited access to supercomputers at their home institutions are given priority. Approved projects are regularly re-evaluated. New projects are approved based on availability and usage load of the BG/L. The Blue Gene/L supercomputer project is aimed at expanding the horizon of high-performance computing to unprecedented levels of scale and performance. Blue Gene/L is the first supercomputer in the Blue Gene family. The full Blue Gene/L consists of 64 racks containing 65,536 high-performance compute nodes. Each node (nodes and chips are the same in the Blue Gene system) contains two embedded 32-bit PowerPC processors. Furthermore, the same chip that is used for compute nodes is also used for the 1,024 I/O nodes. A three-dimensional torus network and a collective network are used to interconnect all nodes. The full system contains 33 terabytes of main memory; it is designed to achieve 183.5 teraflops peak performance using one of the processors of each node for computation and the other processor for communication, and 367 teraflops using both processors for computation. Another key architectural feature of this supercomputer is the link chip component and five Blue Gene/L networks, the PowerPC 440 core and floating-point enhancements, the on-chip and off-chip distributed memory system, the node- and system-level design for high reliability, and the comprehensive approach to fault isolation. One of the key objectives in Blue Gene/L design is to achieve cost/performance comparable to the COTS (Commodity Off The Shelf) approach, while at the same time incorporating a processor and network combination so powerful that it revolutionizes the performance of supercomputer systems. Sponsors: This resource is supported by the INCF.

Proper citation: International Neuroinformatics Coordinating Facility: Blue Gene/L Access (RRID:SCR_001755) Copy   

•   Source: SciCrunch Registry

http://dbserv2.informatik.uni-leipzig.de:8080/onex/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Web-based application that integrates versions of 16 life science ontologies including the Gene Ontology, NCI Thesaurus and selected OBO ontologies with data leading back to 2002 in a common repository to explore ontology changes. It allows to study and apply the evolution of these integrated ontologies on three different levels. It provides global ontology evolution statistics and ontology-specific evolution trends for concepts and relationships and it allows the migration of annotations in case a new ontology version was released

Proper citation: OnEx - Ontology Evolution Explorer (RRID:SCR_000602) Copy   

•   Source: SciCrunch Registry

http://functionalbio.com/web/

A service that provides low cost DNA sequencing. They utilize microfluidic technology.

Proper citation: Functional Biosciences (RRID:SCR_000943) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/rbsurv.html

Software package that selects genes associated with survival.

Proper citation: rbsurv (RRID:SCR_001175) Copy   

•   Source: SciCrunch Registry

http://cran.r-project.org/web/packages/fcros/

A fold change ranks ordering statistics based software for detecting differentially expressed genes.

Proper citation: FCROS (RRID:SCR_006195) Copy   

•   Source: SciCrunch Registry

http://www2.warwick.ac.uk/fac/sci/systemsbiology/staff/ott/tools_and_software/wigwams

A computational tool for analyzing multiple gene expression time series data sets for the same organism. The goal is to determine if there is evidence for gene regulatory mechanisms that are shared by multiple different expression responses.

Proper citation: Wigwams (RRID:SCR_006400) Copy   

•   Source: SciCrunch Registry

http://www.mmnt.net/db/0/0/ftp-genome.wi.mit.edu/distribution/GISTIC2.0

Software to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, they improve the estimation of background rates for each category.

Proper citation: GISTIC (RRID:SCR_000151) Copy   

•   Source: SciCrunch Registry

http://thomsonreuters.com/metadrug/

A leading systems pharmacology solution that incorporates extensive manually curated information on biological effects of small molecule compounds. Predictive and analytical algorithms look at chemical compounds from different angles in one integrated workflow are available for: * Individual previously described compounds to look up their known information and predict currently unknown properties * Individual newly synthesized or isolated compounds to predict their properties from its structures * Compound libraries to extract known and predict new properties of individual compounds and perform their comparison and prioritization

Proper citation: MetaDrug (RRID:SCR_000461) Copy   

•   Source: SciCrunch Registry

http://www.gene-quantification.de/bestkeeper.html

Excel-based tool using pair-wise correlations for determination of stable housekeeping genes, differentially regulated target genes and sample integrity. It determines the best suited standards, out of ten candidates, and combines them into an index. The index can be compared with further ten target genes to decide, whether they are differentially expressed under an applied treatment. All data processing is based on crossing points.

Proper citation: BestKeeper (RRID:SCR_003380) Copy   

•   Source: SciCrunch Registry

http://genecloud.org/

Gene Cloud is a novel tool presenting gene-gene associations based on the scientific literature. It was developed by the Knockout Mouse Repository (www.komp.org) to help our customers find products related to other products they chose. We have built a detailed graph model of gene-gene associations based on how many times two genes are cited in the same article. If two genes are cited in many papers together, they are considered strongly connected. Each instance of Gene Cloud is centered around a specific gene. A list of the top most related genes is plotted as a branching structure from the center. A secondary branch can occur if a gene in the graph is more related a non-central gene than it is to the center gene. The font size of a branched gene indicates the relative strength of connection--always to the center gene. The distribution of genes in space is randomized each time Gene Cloud is run so a different picture will result for the same central gene. Color is used to indicate the availability of Knockout Mouse products at the KOMP Repository. If a gene is colored green in the graph there are products (mutant ES cells, sperm, embryos, or mice) ready to be ordered. Blue colored genes do not yet have products available, but you can follow the links back to the KOMP Repository and register interest to be alerted when products do become available. Gene Cloud is driven by a database of gene-gene associations that currently contains 82,000 genes and other biotypes, 113,000 annotated publications, and 467 million connections. The latest gene symbols, names and gene-publication annotation information is updated daily from the Mouse Genome Informatics database. The graphing is accomplished through the use of a modified version of jsViz.

Proper citation: Gene Cloud: Exploring Connections in the Mouse Genome (RRID:SCR_003503) Copy   

•   Source: SciCrunch Registry

http://www.c2b2.columbia.edu/danapeerlab/html/jistic.html

Software tool for analyzing datasets of genome-wide copy number variation to identify driver aberrations in cancer.

Proper citation: JISTIC (RRID:SCR_003482) Copy   

•   Source: SciCrunch Registry

http://www.affymetrix.com/support/developer/powertools/apt_archive.affx

Affymetrix Power Tools (APT) are a set of cross-platform command line programs that implement algorithms for analyzing and working with Affymetrix GeneChip arrays. APT programs are intended for power users who prefer programs that can be utilized in scripting environments and are sophisticated enough to handle the complexity of extra features and functionality. APT provides platform for developing and deploying new algorithms without waiting for the GUI implementations. This resource is supported by Affymetrix, Inc.

Proper citation: Affymetrix Power Tools (RRID:SCR_008401) Copy   

•   Source: SciCrunch Registry

http://portal.ncibi.org/gateway/gin.html

GIN-IE is a high precision system for extracting protein/gene interactions, interaction cue words, and directionality from the literature. Syntax-aware inferences about the roles of the entities are made by using the syntactic and dependency parse tree structures of the sentences. Negation and speculation are frequently occurring language phenomena that modify the factuality of the information contained in text. GIN-IE detects and distinguishes interactions that are extracted from negated or speculative sentences. GIN-IE has been integrated with the NCIBI PubMed daily update and processing pipeline. The extracted interactions are accessible through MimiWeb.

Proper citation: Gene Interaction Extraction from the Literature (RRID:SCR_008660) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://www.ingenuity.com/products/pathways_analysis.html

A web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.

Proper citation: Ingenuity Pathway Analysis (RRID:SCR_008653) Copy   

•   Source: SciCrunch Registry

https://github.com/manveru/tkgo

Tk-GO is a GUI wrapping the basic functions of the GO AppHandle library from BDGP. GO terms are presented in an explorer-like browser, and behavior can be configured by altering Perl scripts. All available documentation is included in the download. Tk-GO uses the GO database (connects directly to the BDGP database by default) but is user-configurable. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Tk-GO (RRID:SCR_008855) Copy   

•   Source: SciCrunch Registry

http://spotfire.tibco.com/

The Spotfire Gene Ontology Advantage Application integrates GO annotations with gene expression analysis in Spotfire DecisionSite for Functional Genomics. Researchers can select a subset of genes in DecisionSite visualizations and display their distribution in the Gene Ontology hierarchy. Similarly, selection of any process, function or cellular location in the Gene Ontology hierarchy automatically marks the corresponding genes in DecisionSite visualizations. Platform: Windows compatible

Proper citation: Spotfire (RRID:SCR_008858) Copy   

•   Source: SciCrunch Registry

https://wiki.nci.nih.gov/display/caGWAS/caGWAS

Too that allows researchers to integrate, query, report, and analyze significant associations between genetic variations and disease, drug response or other clinical outcomes. SNP array technologies make it possible to genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) simultaneously, enabling whole genome association studies. Within the Clinical Genomic Object Model (CGOM), the caIntegrator team created a domain model for Whole Genome Association Study Analysis. CGOM-caGWAS is a A semantically annotated domain model that captures associations between Study, Study Participant, Disease, SNP Association Analysis, SNP Population Frequency and SNP annotations. caGWAS APIs and web portal provide: * a semantically annotated domain model, database schema with sample data, seasoned middleware, APIs, and web portal for GWAS data; * platform and disease agnostic CGOM-caGWAS model and associated APIs; * the opportunity for developers to customize the look and feel of their GWAS portal; * a foundation of open source technologies; * a well-tested and performance-enhanced platform, as the same software is being used to house the CGEMS data portal; * accelerated analysis of results from various biomedical studies; and * a single application through which researchers and bioinformaticians can access and analyze clinical and experimental data from a variety of data types, as caGWAS objects are part of the CGOM, which includes microarray, genomic, immunohistochemistry, imaging, and clinical data.

Proper citation: caGWAS (RRID:SCR_009617) Copy   

•   Source: SciCrunch Registry

http://www.illumina.com/

American company incorporated that develops, manufactures and markets integrated systems for the analysis of genetic variation and biological function. Provides a line of products and services that serve the sequencing, genotyping and gene expression and proteomics markets. Its headquarters are located in San Diego, California.

Proper citation: Illumina (RRID:SCR_010233) Copy   

•   Source: SciCrunch Registry

http://www.litinspector.org/

A literature search tool providing gene and signal transduction pathway mining within NCBI''''s PubMed database. Its sophisticated gene recognition and intuitive color coding increase the readability of abstracts and lets you analyze signal transduction pathways, diseases and tissue associations in a snap. Note: LitInspector has become part of the Literature & Pathways module of the Genomatix Software Suite.

Proper citation: LitInspector (RRID:SCR_011870) Copy   

•   Source: SciCrunch Registry